TRMF_coefficients: Add a column regularization model to TRMF object
In TRMF: Temporally Regularized Matrix Factorization

Description Usage Arguments Details Value Author(s) References See Also Examples

Adds a regularization model to TRMF object created by create_TRMF() to constrain the fitting process of the coefficient matrix.

TRMF_coefficient is a (soon to be deprecated) alias for TRMF_columns.

TRMF_columns(obj,
    reg_type = c("l2", "nnls", "constrain", "interval", "none"), lambda = 0.0001)
TRMF_coefficients(obj,
    reg_type = c("l2", "nnls", "constrain", "interval", "none"), lambda = 0.0001)

`obj`	TRMF object created by `create_TRMF()`
`reg_type`	regularization type to apply when fitting TRMF model. `l2` regularizes by simple sum of squares, `nnls` forces coefficients to be non-negative. `constrain` constrains coefficients to be non-negative and to sum to 1. `interval` constrains coefficients to the interval [0-1]
`lambda`	L2 regularization parameter used for all regularization types. If NULL, uses lambda set in create_TRMF().

This function doesn't do any computations, it just sets up regularization parameters for the coefficient matrix. This function should only be called once on a TRMF object. If called twice, it will overwrite previous model with a warning.

Returns an updated object of class TRMF.

Chad Hammerquist

Yu, Hsiang-Fu, Nikhil Rao, and Inderjit S. Dhillon. "High-dimensional time series prediction with missing values." arXiv preprint arXiv:1509.08333 (2015).

train.TRMF, create_TRMF, TRMF_trend

# create test data
xm = poly(x = (-10:10)/10,degree=4)
fm = matrix(abs(rnorm(40)),4,10)
Am = xm%*%fm+rnorm(210,0,.2)

# create model
obj = create_TRMF(Am)
obj = TRMF_columns(obj,reg_type ="nnls")
out = train(obj)
plot(out)